Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures

Abstract

Single-crystal diffraction is one of the most common experimental techniques in chemistry for determining a crystal structure. However, the process of crystal structure determination and refinement is not always straightforward. Methods for simplifying and rationalizing the path to the most optimal crystal structure model have been incorporated into various data processing and crystal structure solution software, with the focus generally on aiding macromolecular or protein structure determination. In this work, we propose a new method that uses singlecrystal data to determine the crystal structures of inorganic, extended solids called "single-crystal automated refinement" (SCAR). The approach was developed using data mining and machine learning methods and considers several structural features common in inorganic solids, like atom assignment based on physically reasonable distances, atomic statistical mixing, and crystallographic site deficiency. The output is a tree of possible solutions for the data set with a corresponding fit score indicating the most reasonable crystal structure. Here, the foundation for SCAR is presented followed by the implementation of SCAR to determine two newly synthesized and previously unreported phases, ZrAu 0.5 Os 0.5 and Nd 4 Mn 2 AuGe 4 . The structure solutions are found to be comparable with those produced by manually solving the data set, including the same refined mixed occupancies and atomic deficiency, supporting the validity of this automatic structure solution method. The proposed SCAR program is thus verified as being a fast and reliable assistant in determining even complex single-crystal diffraction data for extended inorganic solids.

Half-Heusler Structures with Full-Heusler Counterparts: Machine-Learning Predictions and Experimental Validation

“…57 Auxiliary machine-learning tools to assist in site assignments may prove helpful. 58 In addition to confirming positive predictions from the machine-learning model, testing negative predictions is important to evaluate the degree to which false negative errors arise. An equal number of "unlikely" candidates (CrGaSn, CrMoNi, CuRuNb, MoHfNi, NiAgNb, VHfAg, ZrRuNb) were identified that have low probabilities (<50%) of adopting half-Heusler structures and that contain at least one component in common with the high-probability candidates characterized above.…”

“…Finally, varying experimental conditions may lead to different types and degrees of disorder . Auxiliary machine-learning tools to assist in site assignments may prove helpful …”

AI Applications through the Whole Life Cycle of Material Discovery

“…Apart from traditional laboratory experiments and computational material simulation approaches, artificial Intelligence (AI) could be an alternative approach that is able to address the material design challenges mentioned above. For example, ML methods have already managed to (1) automate materials' characterization processes and effectively analyze the characterization dataset, [18][19][20][21] (2) quickly screen the vast material design space (e.g., reducing the prediction time of DFT from 10 3 s to 10 À2 s), [22][23][24][25] (3) realize property prediction in complex material systems with limited first-principles understanding, 26 (4) directly map high-dimensional synthesis recipes to materials with desired properties, 27,28 and (5) extract generalizable scientific principles from various material systems. 27,29,30 The reason why AI is particularly apt in material design is due to its inherently strong capabilities in handling huge amounts of data as well as high-dimensional analysis.…”

Twenty years of exceptional success: The molecular education and research consortium in undergraduate computational chemistry (MERCURY)

“…Anton Oliynyk's research: The Oliynyk group studies intermetallic compounds and combines machine learning methods with experimental research. [ 490–495 ] They study the inorganic chemistry of intermetallic materials with focus on energy‐converting materials and mechanical properties such as hardness and wear resistance. [ 493 ] The materials are synthesized directly from elements by high‐temperature methods, including arc‐melting and sintering.…”